Overview

Brought to you by YData

Dataset statistics

Number of variables41
Number of observations567923
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory182.0 MiB
Average record size in memory336.0 B

Variable types

Categorical14
Numeric27

Alerts

COLE_AREA_UBICACION is highly overall correlated with COLE_COD_DANE_ESTABLECIMIENTO and 1 other fieldsHigh correlation
COLE_CALENDARIO is highly overall correlated with PERIODOHigh correlation
COLE_COD_DANE_ESTABLECIMIENTO is highly overall correlated with COLE_AREA_UBICACION and 2 other fieldsHigh correlation
COLE_COD_DANE_SEDE is highly overall correlated with COLE_AREA_UBICACION and 2 other fieldsHigh correlation
COLE_COD_DEPTO_UBICACION is highly overall correlated with COLE_COD_MCPIO_UBICACION and 4 other fieldsHigh correlation
COLE_COD_MCPIO_UBICACION is highly overall correlated with COLE_COD_DEPTO_UBICACION and 4 other fieldsHigh correlation
COLE_DEPTO_UBICACION is highly overall correlated with COLE_COD_DEPTO_UBICACION and 4 other fieldsHigh correlation
COLE_MCPIO_UBICACION is highly overall correlated with ESTU_MCPIO_RESIDEHigh correlation
COLE_NATURALEZA is highly overall correlated with COLE_COD_DANE_ESTABLECIMIENTO and 2 other fieldsHigh correlation
COLE_NOMBRE_ESTABLECIMIENTO is highly overall correlated with COLE_NATURALEZA and 1 other fieldsHigh correlation
COLE_NOMBRE_SEDE is highly overall correlated with COLE_NOMBRE_ESTABLECIMIENTOHigh correlation
DESEMP_INGLES is highly overall correlated with PUNT_INGLESHigh correlation
ESTU_COD_RESIDE_DEPTO is highly overall correlated with COLE_COD_DEPTO_UBICACION and 4 other fieldsHigh correlation
ESTU_COD_RESIDE_MCPIO is highly overall correlated with COLE_COD_DEPTO_UBICACION and 4 other fieldsHigh correlation
ESTU_DEPTO_RESIDE is highly overall correlated with COLE_COD_DEPTO_UBICACION and 4 other fieldsHigh correlation
ESTU_MCPIO_RESIDE is highly overall correlated with COLE_MCPIO_UBICACIONHigh correlation
FAMI_EDUCACIONMADRE is highly overall correlated with FAMI_TIENEAUTOMOVIL and 3 other fieldsHigh correlation
FAMI_TIENEAUTOMOVIL is highly overall correlated with FAMI_EDUCACIONMADRE and 3 other fieldsHigh correlation
FAMI_TIENECOMPUTADOR is highly overall correlated with FAMI_EDUCACIONMADRE and 3 other fieldsHigh correlation
FAMI_TIENEINTERNET is highly overall correlated with FAMI_EDUCACIONMADRE and 3 other fieldsHigh correlation
FAMI_TIENELAVADORA is highly overall correlated with FAMI_EDUCACIONMADRE and 3 other fieldsHigh correlation
PERIODO is highly overall correlated with COLE_CALENDARIOHigh correlation
PUNT_C_NATURALES is highly overall correlated with PUNT_GLOBAL and 4 other fieldsHigh correlation
PUNT_GLOBAL is highly overall correlated with PUNT_C_NATURALES and 4 other fieldsHigh correlation
PUNT_INGLES is highly overall correlated with DESEMP_INGLES and 5 other fieldsHigh correlation
PUNT_LECTURA_CRITICA is highly overall correlated with PUNT_C_NATURALES and 4 other fieldsHigh correlation
PUNT_MATEMATICAS is highly overall correlated with PUNT_C_NATURALES and 4 other fieldsHigh correlation
PUNT_SOCIALES_CIUDADANAS is highly overall correlated with PUNT_C_NATURALES and 4 other fieldsHigh correlation
PERIODO is highly imbalanced (73.3%) Imbalance
COLE_BILINGUE is highly imbalanced (58.6%) Imbalance
COLE_CALENDARIO is highly imbalanced (85.2%) Imbalance
COLE_GENERO is highly imbalanced (83.2%) Imbalance
COLE_SEDE_PRINCIPAL is highly imbalanced (85.4%) Imbalance
FAMI_TIENEAUTOMOVIL is highly imbalanced (51.5%) Imbalance
ESTU_COD_RESIDE_DEPTO is highly skewed (γ1 = 709.7641032) Skewed
ESTU_PAIS_RESIDE is highly skewed (γ1 = 54.04268875) Skewed
ESTU_TIPODOCUMENTO has 109547 (19.3%) zeros Zeros
COLE_JORNADA has 116934 (20.6%) zeros Zeros
FAMI_PERSONASHOGAR has 133821 (23.6%) zeros Zeros

Reproduction

Analysis started2025-05-25 20:21:30.487366
Analysis finished2025-05-25 20:22:54.493098
Duration1 minute and 24.01 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

PERIODO
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.7 MiB
20152
542035 
20151
 
25888

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters2839615
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20152
2nd row20152
3rd row20152
4th row20152
5th row20152

Common Values

ValueCountFrequency (%)
20152 542035
95.4%
20151 25888
 
4.6%

Length

2025-05-25T15:22:54.539365image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-25T15:22:54.600227image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
20152 542035
95.4%
20151 25888
 
4.6%

Most occurring characters

ValueCountFrequency (%)
2 1109958
39.1%
1 593811
20.9%
0 567923
20.0%
5 567923
20.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2839615
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 1109958
39.1%
1 593811
20.9%
0 567923
20.0%
5 567923
20.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2839615
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 1109958
39.1%
1 593811
20.9%
0 567923
20.0%
5 567923
20.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2839615
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 1109958
39.1%
1 593811
20.9%
0 567923
20.0%
5 567923
20.0%

ESTU_TIPODOCUMENTO
Real number (ℝ)

Zeros 

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.342837
Minimum0
Maximum9
Zeros109547
Zeros (%)19.3%
Negative0
Negative (%)0.0%
Memory size8.7 MiB
2025-05-25T15:22:55.755576image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q18
median8
Q38
95-th percentile8
Maximum9
Range9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.2070892
Coefficient of variation (CV)0.50562378
Kurtosis0.073159246
Mean6.342837
Median Absolute Deviation (MAD)0
Skewness-1.4314562
Sum3602243
Variance10.285421
MonotonicityNot monotonic
2025-05-25T15:22:55.794770image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
8 447555
78.8%
0 109547
 
19.3%
2 10399
 
1.8%
1 307
 
0.1%
6 84
 
< 0.1%
5 12
 
< 0.1%
7 10
 
< 0.1%
9 6
 
< 0.1%
3 2
 
< 0.1%
4 1
 
< 0.1%
ValueCountFrequency (%)
0 109547
 
19.3%
1 307
 
0.1%
2 10399
 
1.8%
3 2
 
< 0.1%
4 1
 
< 0.1%
5 12
 
< 0.1%
6 84
 
< 0.1%
7 10
 
< 0.1%
8 447555
78.8%
9 6
 
< 0.1%
ValueCountFrequency (%)
9 6
 
< 0.1%
8 447555
78.8%
7 10
 
< 0.1%
6 84
 
< 0.1%
5 12
 
< 0.1%
4 1
 
< 0.1%
3 2
 
< 0.1%
2 10399
 
1.8%
1 307
 
0.1%
0 109547
 
19.3%

COLE_AREA_UBICACION
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.7 MiB
1
488403 
0
79520 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters567923
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1 488403
86.0%
0 79520
 
14.0%

Length

2025-05-25T15:22:55.836897image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-25T15:22:55.872331image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
1 488403
86.0%
0 79520
 
14.0%

Most occurring characters

ValueCountFrequency (%)
1 488403
86.0%
0 79520
 
14.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 567923
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 488403
86.0%
0 79520
 
14.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 567923
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 488403
86.0%
0 79520
 
14.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 567923
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 488403
86.0%
0 79520
 
14.0%

COLE_BILINGUE
Categorical

Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.7 MiB
0
488851 
2
69544 
1
 
9528

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters567923
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row2
4th row2
5th row0

Common Values

ValueCountFrequency (%)
0 488851
86.1%
2 69544
 
12.2%
1 9528
 
1.7%

Length

2025-05-25T15:22:55.912580image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-25T15:22:55.950014image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
0 488851
86.1%
2 69544
 
12.2%
1 9528
 
1.7%

Most occurring characters

ValueCountFrequency (%)
0 488851
86.1%
2 69544
 
12.2%
1 9528
 
1.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 567923
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 488851
86.1%
2 69544
 
12.2%
1 9528
 
1.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 567923
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 488851
86.1%
2 69544
 
12.2%
1 9528
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 567923
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 488851
86.1%
2 69544
 
12.2%
1 9528
 
1.7%

COLE_CALENDARIO
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.7 MiB
0
549028 
1
 
15054
2
 
3841

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters567923
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 549028
96.7%
1 15054
 
2.7%
2 3841
 
0.7%

Length

2025-05-25T15:22:55.989589image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-25T15:22:56.025015image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
0 549028
96.7%
1 15054
 
2.7%
2 3841
 
0.7%

Most occurring characters

ValueCountFrequency (%)
0 549028
96.7%
1 15054
 
2.7%
2 3841
 
0.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 567923
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 549028
96.7%
1 15054
 
2.7%
2 3841
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 567923
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 549028
96.7%
1 15054
 
2.7%
2 3841
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 567923
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 549028
96.7%
1 15054
 
2.7%
2 3841
 
0.7%

COLE_CARACTER
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.7 MiB
0
313048 
3
189128 
2
60231 
4
 
3636
1
 
1880

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters567923
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row3
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 313048
55.1%
3 189128
33.3%
2 60231
 
10.6%
4 3636
 
0.6%
1 1880
 
0.3%

Length

2025-05-25T15:22:56.064089image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-25T15:22:56.102485image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
0 313048
55.1%
3 189128
33.3%
2 60231
 
10.6%
4 3636
 
0.6%
1 1880
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 313048
55.1%
3 189128
33.3%
2 60231
 
10.6%
4 3636
 
0.6%
1 1880
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 567923
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 313048
55.1%
3 189128
33.3%
2 60231
 
10.6%
4 3636
 
0.6%
1 1880
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 567923
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 313048
55.1%
3 189128
33.3%
2 60231
 
10.6%
4 3636
 
0.6%
1 1880
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 567923
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 313048
55.1%
3 189128
33.3%
2 60231
 
10.6%
4 3636
 
0.6%
1 1880
 
0.3%

COLE_COD_DANE_ESTABLECIMIENTO
Real number (ℝ)

High correlation 

Distinct10038
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1054351 × 1011
Minimum1.05001 × 1011
Maximum5.68432 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.7 MiB
2025-05-25T15:22:56.152547image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1.05001 × 1011
5-th percentile1.05266 × 1011
Q11.19001 × 1011
median1.7600101 × 1011
Q33.11001 × 1011
95-th percentile3.76001 × 1011
Maximum5.68432 × 1011
Range4.63431 × 1011
Interquartile range (IQR)1.92 × 1011

Descriptive statistics

Standard deviation9.544374 × 1010
Coefficient of variation (CV)0.45332074
Kurtosis-1.0103478
Mean2.1054351 × 1011
Median Absolute Deviation (MAD)6.4999993 × 1010
Skewness0.54042814
Sum1.195725 × 1017
Variance9.1095075 × 1021
MonotonicityNot monotonic
2025-05-25T15:22:56.206460image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.110011059 × 10111119
 
0.2%
1.050010001 × 1011987
 
0.2%
3.050010079 × 1011765
 
0.1%
1.050010133 × 1011686
 
0.1%
3.110011054 × 1011675
 
0.1%
1.056150002 × 1011618
 
0.1%
4.050010169 × 1011595
 
0.1%
3.050010174 × 1011592
 
0.1%
1.110010247 × 1011587
 
0.1%
1.760010058 × 1011581
 
0.1%
Other values (10028) 560718
98.7%
ValueCountFrequency (%)
1.05001 × 101124
 
< 0.1%
1.05001 × 101199
 
< 0.1%
1.050010001 × 1011987
0.2%
1.050010001 × 101179
 
< 0.1%
1.050010001 × 101125
 
< 0.1%
1.050010002 × 101131
 
< 0.1%
1.050010002 × 101175
 
< 0.1%
1.050010002 × 101136
 
< 0.1%
1.050010003 × 1011172
 
< 0.1%
1.050010004 × 101150
 
< 0.1%
ValueCountFrequency (%)
5.684320013 × 10117
 
< 0.1%
5.680010097 × 10117
 
< 0.1%
5.25843 × 101138
< 0.1%
5.19142 × 101124
 
< 0.1%
5.19001 × 101194
< 0.1%
4.99760001 × 10117
 
< 0.1%
4.990010019 × 101138
< 0.1%
4.990010012 × 101116
 
< 0.1%
4.950010037 × 101135
 
< 0.1%
4.86865001 × 101112
 
< 0.1%

COLE_COD_DANE_SEDE
Real number (ℝ)

High correlation 

Distinct10281
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1177499 × 1011
Minimum1.05001 × 1011
Maximum8.54874 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.7 MiB
2025-05-25T15:22:56.259221image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1.05001 × 1011
5-th percentile1.05266 × 1011
Q11.19001 × 1011
median1.7600101 × 1011
Q33.11001 × 1011
95-th percentile3.7600101 × 1011
Maximum8.54874 × 1011
Range7.49873 × 1011
Interquartile range (IQR)1.92 × 1011

Descriptive statistics

Standard deviation9.8741446 × 1010
Coefficient of variation (CV)0.46625641
Kurtosis0.89052157
Mean2.1177499 × 1011
Median Absolute Deviation (MAD)6.4999995 × 1010
Skewness0.83057837
Sum1.2027189 × 1017
Variance9.7498731 × 1021
MonotonicityNot monotonic
2025-05-25T15:22:56.315854image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.110011059 × 10111119
 
0.2%
1.050010001 × 1011987
 
0.2%
3.050010079 × 1011765
 
0.1%
1.050010133 × 1011686
 
0.1%
3.110011054 × 1011675
 
0.1%
1.056150002 × 1011618
 
0.1%
4.050010169 × 1011595
 
0.1%
3.050010174 × 1011592
 
0.1%
1.110010247 × 1011587
 
0.1%
1.760010058 × 1011581
 
0.1%
Other values (10271) 560718
98.7%
ValueCountFrequency (%)
1.05001 × 101124
 
< 0.1%
1.05001 × 101199
 
< 0.1%
1.050010001 × 1011987
0.2%
1.050010001 × 101179
 
< 0.1%
1.050010001 × 101125
 
< 0.1%
1.050010002 × 101131
 
< 0.1%
1.050010002 × 101175
 
< 0.1%
1.050010002 × 101136
 
< 0.1%
1.050010003 × 1011172
 
< 0.1%
1.050010004 × 101150
 
< 0.1%
ValueCountFrequency (%)
8.54874 × 10118
 
< 0.1%
8.47001 × 101121
 
< 0.1%
8.47001 × 101174
 
< 0.1%
8.47001 × 101154
 
< 0.1%
8.47001 × 1011193
< 0.1%
8.180011 × 101119
 
< 0.1%
8.180011 × 101180
< 0.1%
8.180011 × 101172
 
< 0.1%
8.180011 × 101142
 
< 0.1%
8.1343 × 101190
< 0.1%

COLE_COD_DEPTO_UBICACION
Real number (ℝ)

High correlation 

Distinct33
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.825744
Minimum5
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.7 MiB
2025-05-25T15:22:56.367990image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile5
Q111
median20
Q354
95-th percentile76
Maximum99
Range94
Interquartile range (IQR)43

Descriptive statistics

Standard deviation26.750042
Coefficient of variation (CV)0.81491046
Kurtosis-1.1774316
Mean32.825744
Median Absolute Deviation (MAD)15
Skewness0.62237497
Sum18642495
Variance715.56475
MonotonicityNot monotonic
2025-05-25T15:22:56.414952image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
11 96691
17.0%
5 76060
13.4%
76 47799
 
8.4%
25 37629
 
6.6%
8 30378
 
5.3%
68 26967
 
4.7%
13 24912
 
4.4%
23 18823
 
3.3%
52 18033
 
3.2%
73 17402
 
3.1%
Other values (23) 173229
30.5%
ValueCountFrequency (%)
5 76060
13.4%
8 30378
 
5.3%
11 96691
17.0%
13 24912
 
4.4%
15 16837
 
3.0%
17 11196
 
2.0%
18 4063
 
0.7%
19 15449
 
2.7%
20 12213
 
2.2%
23 18823
 
3.3%
ValueCountFrequency (%)
99 469
 
0.1%
97 379
 
0.1%
95 935
 
0.2%
94 209
 
< 0.1%
91 827
 
0.1%
88 701
 
0.1%
86 4358
 
0.8%
85 5884
 
1.0%
81 2942
 
0.5%
76 47799
8.4%

COLE_COD_MCPIO_UBICACION
Real number (ℝ)

High correlation 

Distinct1109
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33052.125
Minimum5001
Maximum99773
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.7 MiB
2025-05-25T15:22:56.464647image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum5001
5-th percentile5001
Q111001
median20175
Q354480
95-th percentile76364
Maximum99773
Range94772
Interquartile range (IQR)43479

Descriptive statistics

Standard deviation26775.052
Coefficient of variation (CV)0.81008566
Kurtosis-1.1801106
Mean33052.125
Median Absolute Deviation (MAD)14560
Skewness0.61902283
Sum1.8771062 × 1010
Variance7.1690342 × 108
MonotonicityNot monotonic
2025-05-25T15:22:56.516768image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11001 96691
 
17.0%
5001 31127
 
5.5%
76001 24427
 
4.3%
8001 17104
 
3.0%
13001 13145
 
2.3%
54001 8864
 
1.6%
73001 7774
 
1.4%
68001 7028
 
1.2%
50001 6740
 
1.2%
25754 6701
 
1.2%
Other values (1099) 348322
61.3%
ValueCountFrequency (%)
5001 31127
5.5%
5002 169
 
< 0.1%
5004 29
 
< 0.1%
5021 43
 
< 0.1%
5030 307
 
0.1%
5031 300
 
0.1%
5034 438
 
0.1%
5036 49
 
< 0.1%
5038 103
 
< 0.1%
5040 157
 
< 0.1%
ValueCountFrequency (%)
99773 113
 
< 0.1%
99624 41
 
< 0.1%
99524 109
 
< 0.1%
99001 206
 
< 0.1%
97889 13
 
< 0.1%
97001 366
0.1%
95200 28
 
< 0.1%
95025 136
 
< 0.1%
95015 55
 
< 0.1%
95001 716
0.1%

COLE_DEPTO_UBICACION
Real number (ℝ)

High correlation 

Distinct33
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.774848
Minimum0
Maximum32
Zeros827
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size8.7 MiB
2025-05-25T15:22:56.563264image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q14
median10
Q322
95-th percentile30
Maximum32
Range32
Interquartile range (IQR)18

Descriptive statistics

Standard deviation10.180155
Coefficient of variation (CV)0.79689048
Kurtosis-1.2892781
Mean12.774848
Median Absolute Deviation (MAD)7
Skewness0.44561191
Sum7255130
Variance103.63555
MonotonicityNot monotonic
2025-05-25T15:22:56.611401image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
4 96691
17.0%
1 76060
13.4%
30 47799
 
8.4%
14 37629
 
6.6%
3 30378
 
5.3%
27 26967
 
4.7%
5 24912
 
4.4%
13 18823
 
3.3%
21 18033
 
3.2%
29 17402
 
3.1%
Other values (23) 173229
30.5%
ValueCountFrequency (%)
0 827
 
0.1%
1 76060
13.4%
2 2942
 
0.5%
3 30378
 
5.3%
4 96691
17.0%
5 24912
 
4.4%
6 16837
 
3.0%
7 11196
 
2.0%
8 4063
 
0.7%
9 5884
 
1.0%
ValueCountFrequency (%)
32 469
 
0.1%
31 379
 
0.1%
30 47799
8.4%
29 17402
 
3.1%
28 11366
 
2.0%
27 26967
4.7%
26 701
 
0.1%
25 10721
 
1.9%
24 7101
 
1.3%
23 4358
 
0.8%

COLE_GENERO
Categorical

Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.7 MiB
2
546025 
0
 
16874
1
 
5024

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters567923
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 546025
96.1%
0 16874
 
3.0%
1 5024
 
0.9%

Length

2025-05-25T15:22:56.658605image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-25T15:22:56.694300image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
2 546025
96.1%
0 16874
 
3.0%
1 5024
 
0.9%

Most occurring characters

ValueCountFrequency (%)
2 546025
96.1%
0 16874
 
3.0%
1 5024
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 567923
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 546025
96.1%
0 16874
 
3.0%
1 5024
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 567923
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 546025
96.1%
0 16874
 
3.0%
1 5024
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 567923
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 546025
96.1%
0 16874
 
3.0%
1 5024
 
0.9%

COLE_JORNADA
Real number (ℝ)

Zeros 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4418293
Minimum0
Maximum5
Zeros116934
Zeros (%)20.6%
Negative0
Negative (%)0.0%
Memory size8.7 MiB
2025-05-25T15:22:56.728877image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q32
95-th percentile4
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.2956359
Coefficient of variation (CV)0.89860564
Kurtosis-0.28506253
Mean1.4418293
Median Absolute Deviation (MAD)0
Skewness0.955659
Sum818848
Variance1.6786724
MonotonicityNot monotonic
2025-05-25T15:22:56.770384image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 289092
50.9%
0 116934
20.6%
4 83077
 
14.6%
2 39462
 
6.9%
3 39133
 
6.9%
5 225
 
< 0.1%
ValueCountFrequency (%)
0 116934
20.6%
1 289092
50.9%
2 39462
 
6.9%
3 39133
 
6.9%
4 83077
 
14.6%
5 225
 
< 0.1%
ValueCountFrequency (%)
5 225
 
< 0.1%
4 83077
 
14.6%
3 39133
 
6.9%
2 39462
 
6.9%
1 289092
50.9%
0 116934
20.6%

COLE_MCPIO_UBICACION
Real number (ℝ)

High correlation 

Distinct1026
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean412.08957
Minimum0
Maximum1025
Zeros169
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size8.7 MiB
2025-05-25T15:22:56.818981image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile83
Q1100
median352
Q3642
95-th percentile972
Maximum1025
Range1025
Interquartile range (IQR)542

Descriptive statistics

Standard deviation307.37883
Coefficient of variation (CV)0.74590297
Kurtosis-1.1372441
Mean412.08957
Median Absolute Deviation (MAD)252
Skewness0.46758775
Sum2.3403514 × 108
Variance94481.748
MonotonicityNot monotonic
2025-05-25T15:22:56.906619image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 96691
 
17.0%
512 31127
 
5.5%
132 24427
 
4.3%
83 17104
 
3.0%
156 13145
 
2.3%
240 8864
 
1.6%
393 7774
 
1.4%
107 7028
 
1.2%
999 6740
 
1.2%
855 6701
 
1.2%
Other values (1016) 348322
61.3%
ValueCountFrequency (%)
0 169
 
< 0.1%
1 260
 
< 0.1%
2 29
 
< 0.1%
3 1021
0.2%
4 78
 
< 0.1%
5 378
 
0.1%
6 319
 
0.1%
7 115
 
< 0.1%
8 131
 
< 0.1%
9 1158
0.2%
ValueCountFrequency (%)
1025 774
0.1%
1024 1723
0.3%
1023 64
 
< 0.1%
1022 77
 
< 0.1%
1021 407
 
0.1%
1020 294
 
0.1%
1019 88
 
< 0.1%
1018 140
 
< 0.1%
1017 165
 
< 0.1%
1016 1414
0.2%

COLE_NATURALEZA
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.7 MiB
1
405598 
0
162325 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters567923
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
1 405598
71.4%
0 162325
28.6%

Length

2025-05-25T15:22:56.967666image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-25T15:22:57.003467image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
1 405598
71.4%
0 162325
28.6%

Most occurring characters

ValueCountFrequency (%)
1 405598
71.4%
0 162325
28.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 567923
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 405598
71.4%
0 162325
28.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 567923
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 405598
71.4%
0 162325
28.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 567923
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 405598
71.4%
0 162325
28.6%

COLE_NOMBRE_ESTABLECIMIENTO
Real number (ℝ)

High correlation 

Distinct9102
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4587.9136
Minimum0
Maximum9109
Zeros12
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size8.7 MiB
2025-05-25T15:22:57.047284image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile564
Q12293
median4739
Q36742
95-th percentile8495
Maximum9109
Range9109
Interquartile range (IQR)4449

Descriptive statistics

Standard deviation2537.2982
Coefficient of variation (CV)0.55303967
Kurtosis-1.19626
Mean4587.9136
Median Absolute Deviation (MAD)2211
Skewness-0.070990057
Sum2.6055817 × 109
Variance6437882.3
MonotonicityNot monotonic
2025-05-25T15:22:57.101126image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7311 1352
 
0.2%
2499 1300
 
0.2%
311 1189
 
0.2%
6494 1168
 
0.2%
7440 1157
 
0.2%
5309 1081
 
0.2%
6306 1057
 
0.2%
8496 1031
 
0.2%
5485 987
 
0.2%
7293 939
 
0.2%
Other values (9092) 556662
98.0%
ValueCountFrequency (%)
0 12
 
< 0.1%
1 210
< 0.1%
2 178
< 0.1%
3 31
 
< 0.1%
4 29
 
< 0.1%
5 67
 
< 0.1%
6 21
 
< 0.1%
7 8
 
< 0.1%
8 141
< 0.1%
9 81
 
< 0.1%
ValueCountFrequency (%)
9109 29
 
< 0.1%
9108 31
 
< 0.1%
9107 26
 
< 0.1%
9106 14
 
< 0.1%
9105 1
 
< 0.1%
9104 81
< 0.1%
9103 45
< 0.1%
9102 103
< 0.1%
9101 22
 
< 0.1%
9100 43
< 0.1%

COLE_NOMBRE_SEDE
Real number (ℝ)

High correlation 

Distinct9616
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4873.0311
Minimum1
Maximum9624
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.7 MiB
2025-05-25T15:22:57.156193image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile410
Q12233
median5170
Q37354
95-th percentile9218
Maximum9624
Range9623
Interquartile range (IQR)5121

Descriptive statistics

Standard deviation2871.9873
Coefficient of variation (CV)0.58936364
Kurtosis-1.2679982
Mean4873.0311
Median Absolute Deviation (MAD)2473
Skewness-0.079319626
Sum2.7675064 × 109
Variance8248311.2
MonotonicityNot monotonic
2025-05-25T15:22:57.210064image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
698 1119
 
0.2%
6219 987
 
0.2%
8194 865
 
0.2%
8264 841
 
0.1%
4264 765
 
0.1%
6313 686
 
0.1%
2371 675
 
0.1%
8360 664
 
0.1%
3640 625
 
0.1%
4540 618
 
0.1%
Other values (9606) 560078
98.6%
ValueCountFrequency (%)
1 39
 
< 0.1%
2 46
 
< 0.1%
3 58
 
< 0.1%
4 25
 
< 0.1%
5 29
 
< 0.1%
6 367
0.1%
7 118
 
< 0.1%
12 178
< 0.1%
13 24
 
< 0.1%
14 105
 
< 0.1%
ValueCountFrequency (%)
9624 29
< 0.1%
9623 15
 
< 0.1%
9622 8
 
< 0.1%
9621 11
 
< 0.1%
9620 26
< 0.1%
9619 9
 
< 0.1%
9618 26
< 0.1%
9617 15
 
< 0.1%
9616 3
 
< 0.1%
9615 55
< 0.1%

COLE_SEDE_PRINCIPAL
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.7 MiB
9
556147 
8
 
11776

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters567923
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9
2nd row9
3rd row9
4th row9
5th row9

Common Values

ValueCountFrequency (%)
9 556147
97.9%
8 11776
 
2.1%

Length

2025-05-25T15:22:57.258067image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-25T15:22:57.293378image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
9 556147
97.9%
8 11776
 
2.1%

Most occurring characters

ValueCountFrequency (%)
9 556147
97.9%
8 11776
 
2.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 567923
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
9 556147
97.9%
8 11776
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 567923
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
9 556147
97.9%
8 11776
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 567923
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
9 556147
97.9%
8 11776
 
2.1%

ESTU_COD_RESIDE_DEPTO
Real number (ℝ)

High correlation  Skewed 

Distinct34
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.992025
Minimum5
Maximum99999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.7 MiB
2025-05-25T15:22:57.332464image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile5
Q111
median20
Q354
95-th percentile76
Maximum99999
Range99994
Interquartile range (IQR)43

Descriptive statistics

Standard deviation135.32756
Coefficient of variation (CV)4.1018263
Kurtosis524297.58
Mean32.992025
Median Absolute Deviation (MAD)15
Skewness709.7641
Sum18736930
Variance18313.547
MonotonicityNot monotonic
2025-05-25T15:22:57.382009image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
11 97830
17.2%
5 76108
13.4%
76 48104
 
8.5%
25 36354
 
6.4%
8 30314
 
5.3%
68 26631
 
4.7%
13 24865
 
4.4%
23 18857
 
3.3%
52 18037
 
3.2%
73 17498
 
3.1%
Other values (24) 173325
30.5%
ValueCountFrequency (%)
5 76108
13.4%
8 30314
 
5.3%
11 97830
17.2%
13 24865
 
4.4%
15 16964
 
3.0%
17 11312
 
2.0%
18 4049
 
0.7%
19 15232
 
2.7%
20 12210
 
2.1%
23 18857
 
3.3%
ValueCountFrequency (%)
99999 1
 
< 0.1%
99 473
 
0.1%
97 379
 
0.1%
95 941
 
0.2%
94 208
 
< 0.1%
91 832
 
0.1%
88 760
 
0.1%
86 4368
0.8%
85 5904
1.0%
81 2972
0.5%

ESTU_COD_RESIDE_MCPIO
Real number (ℝ)

High correlation 

Distinct1114
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33040.772
Minimum5001
Maximum99999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.7 MiB
2025-05-25T15:22:57.432534image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum5001
5-th percentile5001
Q111001
median20032
Q354498
95-th percentile76364
Maximum99999
Range94998
Interquartile range (IQR)43497

Descriptive statistics

Standard deviation26811.553
Coefficient of variation (CV)0.81146873
Kurtosis-1.1815521
Mean33040.772
Median Absolute Deviation (MAD)14425
Skewness0.61971393
Sum1.8764614 × 1010
Variance7.188594 × 108
MonotonicityNot monotonic
2025-05-25T15:22:57.484510image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11001 97830
 
17.2%
5001 30746
 
5.4%
76001 24300
 
4.3%
8001 17221
 
3.0%
13001 13193
 
2.3%
54001 8845
 
1.6%
73001 7842
 
1.4%
25754 6781
 
1.2%
68001 6745
 
1.2%
50001 6656
 
1.2%
Other values (1104) 347764
61.2%
ValueCountFrequency (%)
5001 30746
5.4%
5002 205
 
< 0.1%
5004 32
 
< 0.1%
5021 43
 
< 0.1%
5030 334
 
0.1%
5031 304
 
0.1%
5034 465
 
0.1%
5036 43
 
< 0.1%
5038 145
 
< 0.1%
5040 216
 
< 0.1%
ValueCountFrequency (%)
99999 1
 
< 0.1%
99773 114
 
< 0.1%
99624 42
 
< 0.1%
99524 110
 
< 0.1%
99001 207
< 0.1%
97889 13
 
< 0.1%
97666 26
 
< 0.1%
97161 30
 
< 0.1%
97001 310
0.1%
95200 29
 
< 0.1%

ESTU_DEPTO_RESIDE
Real number (ℝ)

High correlation 

Distinct34
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.126241
Minimum2
Maximum35
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.7 MiB
2025-05-25T15:22:57.529412image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3
Q16
median12
Q325
95-th percentile33
Maximum35
Range33
Interquartile range (IQR)19

Descriptive statistics

Standard deviation10.629877
Coefficient of variation (CV)0.70274416
Kurtosis-1.311803
Mean15.126241
Median Absolute Deviation (MAD)8
Skewness0.45542413
Sum8590540
Variance112.99429
MonotonicityNot monotonic
2025-05-25T15:22:57.576410image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
6 97830
17.2%
3 76108
13.4%
33 48104
 
8.5%
16 36354
 
6.4%
5 30314
 
5.3%
30 26631
 
4.7%
7 24865
 
4.4%
15 18857
 
3.3%
24 18037
 
3.2%
32 17498
 
3.1%
Other values (24) 173325
30.5%
ValueCountFrequency (%)
2 832
 
0.1%
3 76108
13.4%
4 2972
 
0.5%
5 30314
 
5.3%
6 97830
17.2%
7 24865
 
4.4%
8 16964
 
3.0%
9 11312
 
2.0%
10 4049
 
0.7%
11 5904
 
1.0%
ValueCountFrequency (%)
35 473
 
0.1%
34 379
 
0.1%
33 48104
8.5%
32 17498
 
3.1%
31 11342
 
2.0%
30 26631
4.7%
29 760
 
0.1%
28 10747
 
1.9%
27 6984
 
1.2%
26 4368
 
0.8%

ESTU_FECHANACIMIENTO
Real number (ℝ)

Distinct12847
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6465.7622
Minimum0
Maximum12849
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size8.7 MiB
2025-05-25T15:22:57.628097image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile715
Q13260
median6486
Q39639
95-th percentile12238
Maximum12849
Range12849
Interquartile range (IQR)6379

Descriptive statistics

Standard deviation3698.0934
Coefficient of variation (CV)0.57195011
Kurtosis-1.1957068
Mean6465.7622
Median Absolute Deviation (MAD)3189
Skewness-0.0025952148
Sum3.6720551 × 109
Variance13675895
MonotonicityNot monotonic
2025-05-25T15:22:57.683909image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7133 820
 
0.1%
7544 818
 
0.1%
8806 815
 
0.1%
9230 808
 
0.1%
6313 794
 
0.1%
824 792
 
0.1%
2519 788
 
0.1%
4677 778
 
0.1%
7650 777
 
0.1%
10064 772
 
0.1%
Other values (12837) 559961
98.6%
ValueCountFrequency (%)
0 2
< 0.1%
1 1
 
< 0.1%
2 1
 
< 0.1%
3 2
< 0.1%
4 1
 
< 0.1%
5 3
< 0.1%
6 1
 
< 0.1%
7 2
< 0.1%
8 2
< 0.1%
9 3
< 0.1%
ValueCountFrequency (%)
12849 2
 
< 0.1%
12848 4
 
< 0.1%
12847 87
 
< 0.1%
12846 665
0.1%
12845 372
0.1%
12844 190
 
< 0.1%
12843 67
 
< 0.1%
12842 35
 
< 0.1%
12841 11
 
< 0.1%
12840 9
 
< 0.1%

ESTU_GENERO
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.7 MiB
335
309076 
336
256419 
337
 
2428

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1703769
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row336
2nd row336
3rd row336
4th row336
5th row335

Common Values

ValueCountFrequency (%)
335 309076
54.4%
336 256419
45.2%
337 2428
 
0.4%

Length

2025-05-25T15:22:57.734040image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-25T15:22:57.770633image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
335 309076
54.4%
336 256419
45.2%
337 2428
 
0.4%

Most occurring characters

ValueCountFrequency (%)
3 1135846
66.7%
5 309076
 
18.1%
6 256419
 
15.1%
7 2428
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1703769
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 1135846
66.7%
5 309076
 
18.1%
6 256419
 
15.1%
7 2428
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1703769
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 1135846
66.7%
5 309076
 
18.1%
6 256419
 
15.1%
7 2428
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1703769
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 1135846
66.7%
5 309076
 
18.1%
6 256419
 
15.1%
7 2428
 
0.1%

ESTU_MCPIO_RESIDE
Real number (ℝ)

High correlation 

Distinct1031
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean411.45271
Minimum0
Maximum1033
Zeros205
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size8.7 MiB
2025-05-25T15:22:57.816201image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile82
Q199
median354
Q3643
95-th percentile976
Maximum1033
Range1033
Interquartile range (IQR)544

Descriptive statistics

Standard deviation307.74001
Coefficient of variation (CV)0.74793532
Kurtosis-1.1325442
Mean411.45271
Median Absolute Deviation (MAD)255
Skewness0.46460955
Sum2.3367346 × 108
Variance94703.914
MonotonicityNot monotonic
2025-05-25T15:22:57.869960image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
99 97830
 
17.2%
515 30746
 
5.4%
128 24300
 
4.3%
82 17221
 
3.0%
149 13193
 
2.3%
249 8845
 
1.6%
400 7842
 
1.4%
859 6781
 
1.2%
106 6745
 
1.2%
1001 6656
 
1.2%
Other values (1021) 347764
61.2%
ValueCountFrequency (%)
0 205
 
< 0.1%
1 32
 
< 0.1%
2 1032
0.2%
3 79
 
< 0.1%
4 232
 
< 0.1%
5 294
 
0.1%
6 106
 
< 0.1%
7 126
 
< 0.1%
8 1148
0.2%
9 24
 
< 0.1%
ValueCountFrequency (%)
1033 53
 
< 0.1%
1032 93
 
< 0.1%
1031 125
 
< 0.1%
1030 239
 
< 0.1%
1028 778
0.1%
1027 1783
0.3%
1026 67
 
< 0.1%
1025 74
 
< 0.1%
1024 440
 
0.1%
1023 300
 
0.1%

ESTU_PAIS_RESIDE
Real number (ℝ)

Skewed 

Distinct54
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.009065
Minimum0
Maximum55
Zeros7
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size8.7 MiB
2025-05-25T15:22:57.919466image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile19
Q119
median19
Q319
95-th percentile19
Maximum55
Range55
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.54380053
Coefficient of variation (CV)0.028607432
Kurtosis3446.7415
Mean19.009065
Median Absolute Deviation (MAD)0
Skewness54.042689
Sum10795685
Variance0.29571902
MonotonicityNot monotonic
2025-05-25T15:22:57.970339image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19 567568
99.9%
55 84
 
< 0.1%
31 53
 
< 0.1%
27 23
 
< 0.1%
30 18
 
< 0.1%
22 14
 
< 0.1%
20 13
 
< 0.1%
40 12
 
< 0.1%
5 11
 
< 0.1%
43 10
 
< 0.1%
Other values (44) 117
 
< 0.1%
ValueCountFrequency (%)
0 7
< 0.1%
1 1
 
< 0.1%
2 1
 
< 0.1%
3 1
 
< 0.1%
4 1
 
< 0.1%
5 11
< 0.1%
6 1
 
< 0.1%
7 1
 
< 0.1%
8 1
 
< 0.1%
9 6
< 0.1%
ValueCountFrequency (%)
55 84
< 0.1%
54 3
 
< 0.1%
53 1
 
< 0.1%
52 1
 
< 0.1%
51 1
 
< 0.1%
50 1
 
< 0.1%
49 2
 
< 0.1%
48 3
 
< 0.1%
47 5
 
< 0.1%
46 1
 
< 0.1%

FAMI_CUARTOSHOGAR
Real number (ℝ)

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.495907
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.7 MiB
2025-05-25T15:22:58.016307image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q14
median4
Q310
95-th percentile10
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.4550225
Coefficient of variation (CV)0.53187683
Kurtosis-1.7384593
Mean6.495907
Median Absolute Deviation (MAD)3
Skewness-0.035496842
Sum3689175
Variance11.93718
MonotonicityNot monotonic
2025-05-25T15:22:58.057398image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
10 238122
41.9%
4 202961
35.7%
2 73540
 
12.9%
11 20873
 
3.7%
1 20243
 
3.6%
8 6573
 
1.2%
9 2307
 
0.4%
7 1238
 
0.2%
12 1088
 
0.2%
3 584
 
0.1%
ValueCountFrequency (%)
1 20243
 
3.6%
2 73540
 
12.9%
3 584
 
0.1%
4 202961
35.7%
6 394
 
0.1%
7 1238
 
0.2%
8 6573
 
1.2%
9 2307
 
0.4%
10 238122
41.9%
11 20873
 
3.7%
ValueCountFrequency (%)
12 1088
 
0.2%
11 20873
 
3.7%
10 238122
41.9%
9 2307
 
0.4%
8 6573
 
1.2%
7 1238
 
0.2%
6 394
 
0.1%
4 202961
35.7%
3 584
 
0.1%
2 73540
 
12.9%

FAMI_EDUCACIONMADRE
Real number (ℝ)

High correlation 

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.999986
Minimum4
Maximum21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.7 MiB
2025-05-25T15:22:58.096291image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile4
Q111
median13
Q313
95-th percentile18
Maximum21
Range17
Interquartile range (IQR)2

Descriptive statistics

Standard deviation3.4557412
Coefficient of variation (CV)0.28797877
Kurtosis0.98108463
Mean11.999986
Median Absolute Deviation (MAD)1
Skewness-0.68899166
Sum6815068
Variance11.942147
MonotonicityNot monotonic
2025-05-25T15:22:58.137295image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
13 158986
28.0%
11 94109
16.6%
14 86906
15.3%
12 84650
14.9%
4 50070
 
8.8%
18 40561
 
7.1%
7 12491
 
2.2%
10 11590
 
2.0%
19 11539
 
2.0%
8 8366
 
1.5%
Other values (2) 8655
 
1.5%
ValueCountFrequency (%)
4 50070
 
8.8%
5 7567
 
1.3%
7 12491
 
2.2%
8 8366
 
1.5%
10 11590
 
2.0%
11 94109
16.6%
12 84650
14.9%
13 158986
28.0%
14 86906
15.3%
18 40561
 
7.1%
ValueCountFrequency (%)
21 1088
 
0.2%
19 11539
 
2.0%
18 40561
 
7.1%
14 86906
15.3%
13 158986
28.0%
12 84650
14.9%
11 94109
16.6%
10 11590
 
2.0%
8 8366
 
1.5%
7 12491
 
2.2%

FAMI_EDUCACIONPADRE
Real number (ℝ)

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.8332644
Minimum2
Maximum15
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.7 MiB
2025-05-25T15:22:58.179142image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2
Q17
median8
Q39
95-th percentile13
Maximum15
Range13
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.7363163
Coefficient of variation (CV)0.34932004
Kurtosis0.36608831
Mean7.8332644
Median Absolute Deviation (MAD)1
Skewness-0.36345494
Sum4448691
Variance7.4874266
MonotonicityNot monotonic
2025-05-25T15:22:58.223062image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
9 139042
24.5%
8 99593
17.5%
7 96640
17.0%
10 76085
13.4%
2 47395
 
8.3%
13 31145
 
5.5%
4 25936
 
4.6%
5 22217
 
3.9%
6 12111
 
2.1%
14 9976
 
1.8%
Other values (2) 7783
 
1.4%
ValueCountFrequency (%)
2 47395
 
8.3%
3 6695
 
1.2%
4 25936
 
4.6%
5 22217
 
3.9%
6 12111
 
2.1%
7 96640
17.0%
8 99593
17.5%
9 139042
24.5%
10 76085
13.4%
13 31145
 
5.5%
ValueCountFrequency (%)
15 1088
 
0.2%
14 9976
 
1.8%
13 31145
 
5.5%
10 76085
13.4%
9 139042
24.5%
8 99593
17.5%
7 96640
17.0%
6 12111
 
2.1%
5 22217
 
3.9%
4 25936
 
4.6%

FAMI_ESTRATOVIVIENDA
Real number (ℝ)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.8915557
Minimum2
Maximum17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.7 MiB
2025-05-25T15:22:58.261220image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2
Q12
median3
Q33
95-th percentile5
Maximum17
Range15
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.0210741
Coefficient of variation (CV)0.35312275
Kurtosis9.9852352
Mean2.8915557
Median Absolute Deviation (MAD)1
Skewness1.8110993
Sum1642181
Variance1.0425924
MonotonicityNot monotonic
2025-05-25T15:22:58.307862image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2 244474
43.0%
3 193719
34.1%
4 95694
 
16.8%
5 21150
 
3.7%
6 7962
 
1.4%
7 4793
 
0.8%
17 131
 
< 0.1%
ValueCountFrequency (%)
2 244474
43.0%
3 193719
34.1%
4 95694
 
16.8%
5 21150
 
3.7%
6 7962
 
1.4%
7 4793
 
0.8%
17 131
 
< 0.1%
ValueCountFrequency (%)
17 131
 
< 0.1%
7 4793
 
0.8%
6 7962
 
1.4%
5 21150
 
3.7%
4 95694
 
16.8%
3 193719
34.1%
2 244474
43.0%

FAMI_PERSONASHOGAR
Real number (ℝ)

Zeros 

Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.1706587
Minimum0
Maximum24
Zeros133821
Zeros (%)23.6%
Negative0
Negative (%)0.0%
Memory size8.7 MiB
2025-05-25T15:22:58.352467image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q319
95-th percentile21
Maximum24
Range24
Interquartile range (IQR)18

Descriptive statistics

Standard deviation9.2300168
Coefficient of variation (CV)1.1296539
Kurtosis-1.6643189
Mean8.1706587
Median Absolute Deviation (MAD)1
Skewness0.49215393
Sum4640305
Variance85.193211
MonotonicityNot monotonic
2025-05-25T15:22:58.395356image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
1 173763
30.6%
0 133821
23.6%
21 100417
17.7%
19 63658
 
11.2%
4 30132
 
5.3%
20 29556
 
5.2%
13 15452
 
2.7%
12 6507
 
1.1%
2 4480
 
0.8%
23 3653
 
0.6%
Other values (3) 6484
 
1.1%
ValueCountFrequency (%)
0 133821
23.6%
1 173763
30.6%
2 4480
 
0.8%
3 3360
 
0.6%
4 30132
 
5.3%
12 6507
 
1.1%
13 15452
 
2.7%
14 2036
 
0.4%
19 63658
 
11.2%
20 29556
 
5.2%
ValueCountFrequency (%)
24 1088
 
0.2%
23 3653
 
0.6%
21 100417
17.7%
20 29556
 
5.2%
19 63658
11.2%
14 2036
 
0.4%
13 15452
 
2.7%
12 6507
 
1.1%
4 30132
 
5.3%
3 3360
 
0.6%

FAMI_TIENEAUTOMOVIL
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.7 MiB
8
445086 
13
121749 
16
 
1088

Length

Max length2
Median length1
Mean length1.2162916
Min length1

Characters and Unicode

Total characters690760
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8
2nd row8
3rd row8
4th row8
5th row8

Common Values

ValueCountFrequency (%)
8 445086
78.4%
13 121749
 
21.4%
16 1088
 
0.2%

Length

2025-05-25T15:22:58.440344image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-25T15:22:58.477428image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
8 445086
78.4%
13 121749
 
21.4%
16 1088
 
0.2%

Most occurring characters

ValueCountFrequency (%)
8 445086
64.4%
1 122837
 
17.8%
3 121749
 
17.6%
6 1088
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 690760
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
8 445086
64.4%
1 122837
 
17.8%
3 121749
 
17.6%
6 1088
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 690760
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
8 445086
64.4%
1 122837
 
17.8%
3 121749
 
17.6%
6 1088
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 690760
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
8 445086
64.4%
1 122837
 
17.8%
3 121749
 
17.6%
6 1088
 
0.2%

FAMI_TIENECOMPUTADOR
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.7 MiB
5
349678 
3
217157 
7
 
1088

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters567923
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row5
3rd row5
4th row5
5th row3

Common Values

ValueCountFrequency (%)
5 349678
61.6%
3 217157
38.2%
7 1088
 
0.2%

Length

2025-05-25T15:22:58.517533image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-25T15:22:58.553288image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
5 349678
61.6%
3 217157
38.2%
7 1088
 
0.2%

Most occurring characters

ValueCountFrequency (%)
5 349678
61.6%
3 217157
38.2%
7 1088
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 567923
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
5 349678
61.6%
3 217157
38.2%
7 1088
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 567923
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
5 349678
61.6%
3 217157
38.2%
7 1088
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 567923
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
5 349678
61.6%
3 217157
38.2%
7 1088
 
0.2%

FAMI_TIENEINTERNET
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.7 MiB
1
301104 
0
265731 
2
 
1088

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters567923
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1 301104
53.0%
0 265731
46.8%
2 1088
 
0.2%

Length

2025-05-25T15:22:58.594655image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-25T15:22:58.630453image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
1 301104
53.0%
0 265731
46.8%
2 1088
 
0.2%

Most occurring characters

ValueCountFrequency (%)
1 301104
53.0%
0 265731
46.8%
2 1088
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 567923
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 301104
53.0%
0 265731
46.8%
2 1088
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 567923
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 301104
53.0%
0 265731
46.8%
2 1088
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 567923
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 301104
53.0%
0 265731
46.8%
2 1088
 
0.2%

FAMI_TIENELAVADORA
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.7 MiB
1
383495 
0
183340 
2
 
1088

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters567923
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 383495
67.5%
0 183340
32.3%
2 1088
 
0.2%

Length

2025-05-25T15:22:58.670355image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-25T15:22:58.707088image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
1 383495
67.5%
0 183340
32.3%
2 1088
 
0.2%

Most occurring characters

ValueCountFrequency (%)
1 383495
67.5%
0 183340
32.3%
2 1088
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 567923
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 383495
67.5%
0 183340
32.3%
2 1088
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 567923
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 383495
67.5%
0 183340
32.3%
2 1088
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 567923
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 383495
67.5%
0 183340
32.3%
2 1088
 
0.2%

DESEMP_INGLES
Categorical

High correlation 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.7 MiB
0
256434 
1
218721 
2
46734 
4
31846 
3
 
14188

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters567923
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 256434
45.2%
1 218721
38.5%
2 46734
 
8.2%
4 31846
 
5.6%
3 14188
 
2.5%

Length

2025-05-25T15:22:58.746326image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-25T15:22:58.784819image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
0 256434
45.2%
1 218721
38.5%
2 46734
 
8.2%
4 31846
 
5.6%
3 14188
 
2.5%

Most occurring characters

ValueCountFrequency (%)
0 256434
45.2%
1 218721
38.5%
2 46734
 
8.2%
4 31846
 
5.6%
3 14188
 
2.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 567923
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 256434
45.2%
1 218721
38.5%
2 46734
 
8.2%
4 31846
 
5.6%
3 14188
 
2.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 567923
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 256434
45.2%
1 218721
38.5%
2 46734
 
8.2%
4 31846
 
5.6%
3 14188
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 567923
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 256434
45.2%
1 218721
38.5%
2 46734
 
8.2%
4 31846
 
5.6%
3 14188
 
2.5%

PUNT_INGLES
Real number (ℝ)

High correlation 

Distinct73
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.729999
Minimum0
Maximum100
Zeros222
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size8.7 MiB
2025-05-25T15:22:58.833762image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile38
Q143
median49
Q354
95-th percentile76
Maximum100
Range100
Interquartile range (IQR)11

Descriptive statistics

Standard deviation11.469342
Coefficient of variation (CV)0.22608598
Kurtosis2.875239
Mean50.729999
Median Absolute Deviation (MAD)6
Skewness1.4650751
Sum28810733
Variance131.5458
MonotonicityNot monotonic
2025-05-25T15:22:58.889305image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
46 49279
 
8.7%
49 49215
 
8.7%
44 48049
 
8.5%
43 44840
 
7.9%
50 42645
 
7.5%
41 36880
 
6.5%
52 35704
 
6.3%
53 28832
 
5.1%
40 28249
 
5.0%
54 22234
 
3.9%
Other values (63) 181996
32.0%
ValueCountFrequency (%)
0 222
< 0.1%
7 48
 
< 0.1%
14 76
 
< 0.1%
17 2
 
< 0.1%
19 7
 
< 0.1%
20 141
 
< 0.1%
21 1
 
< 0.1%
23 8
 
< 0.1%
24 2
 
< 0.1%
25 418
0.1%
ValueCountFrequency (%)
100 631
 
0.1%
97 1029
 
0.2%
95 875
 
0.2%
94 1627
0.3%
92 31
 
< 0.1%
91 2152
0.4%
90 1215
 
0.2%
88 2589
0.5%
87 43
 
< 0.1%
85 3996
0.7%

PUNT_MATEMATICAS
Real number (ℝ)

High correlation 

Distinct94
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.360163
Minimum0
Maximum100
Zeros54
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size8.7 MiB
2025-05-25T15:22:58.943112image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile33
Q142
median49
Q357
95-th percentile72
Maximum100
Range100
Interquartile range (IQR)15

Descriptive statistics

Standard deviation12.276876
Coefficient of variation (CV)0.24378149
Kurtosis1.0788616
Mean50.360163
Median Absolute Deviation (MAD)7
Skewness0.67511454
Sum28600695
Variance150.72168
MonotonicityNot monotonic
2025-05-25T15:22:58.998027image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
47 33436
 
5.9%
49 32513
 
5.7%
44 31394
 
5.5%
52 30534
 
5.4%
42 28951
 
5.1%
54 26738
 
4.7%
46 25643
 
4.5%
55 25446
 
4.5%
50 25350
 
4.5%
57 21545
 
3.8%
Other values (84) 286373
50.4%
ValueCountFrequency (%)
0 54
< 0.1%
1 9
 
< 0.1%
2 3
 
< 0.1%
7 19
 
< 0.1%
8 30
< 0.1%
10 3
 
< 0.1%
11 4
 
< 0.1%
12 49
< 0.1%
13 74
< 0.1%
14 1
 
< 0.1%
ValueCountFrequency (%)
100 1376
0.2%
99 24
 
< 0.1%
97 1
 
< 0.1%
96 686
0.1%
95 134
 
< 0.1%
93 700
0.1%
92 341
 
0.1%
91 126
 
< 0.1%
90 591
0.1%
89 755
0.1%

PUNT_SOCIALES_CIUDADANAS
Real number (ℝ)

High correlation 

Distinct97
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.05006
Minimum0
Maximum100
Zeros55
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size8.7 MiB
2025-05-25T15:22:59.051425image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile32
Q143
median50
Q357
95-th percentile68
Maximum100
Range100
Interquartile range (IQR)14

Descriptive statistics

Standard deviation11.424929
Coefficient of variation (CV)0.22827004
Kurtosis0.20865237
Mean50.05006
Median Absolute Deviation (MAD)7
Skewness0.15348568
Sum28424580
Variance130.52901
MonotonicityNot monotonic
2025-05-25T15:22:59.105779image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50 27852
 
4.9%
53 26898
 
4.7%
49 26869
 
4.7%
46 26151
 
4.6%
43 24527
 
4.3%
56 24276
 
4.3%
47 21151
 
3.7%
52 20752
 
3.7%
59 20736
 
3.7%
38 20330
 
3.6%
Other values (87) 328381
57.8%
ValueCountFrequency (%)
0 55
< 0.1%
3 38
 
< 0.1%
4 10
 
< 0.1%
5 5
 
< 0.1%
6 3
 
< 0.1%
7 4
 
< 0.1%
9 95
< 0.1%
10 45
< 0.1%
11 11
 
< 0.1%
12 12
 
< 0.1%
ValueCountFrequency (%)
100 96
< 0.1%
99 24
 
< 0.1%
98 100
< 0.1%
97 44
 
< 0.1%
96 31
 
< 0.1%
95 5
 
< 0.1%
94 2
 
< 0.1%
93 46
 
< 0.1%
92 211
< 0.1%
91 138
< 0.1%

PUNT_C_NATURALES
Real number (ℝ)

High correlation 

Distinct86
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.278105
Minimum0
Maximum100
Zeros70
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size8.7 MiB
2025-05-25T15:22:59.161197image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile35
Q143
median50
Q356
95-th percentile69
Maximum100
Range100
Interquartile range (IQR)13

Descriptive statistics

Standard deviation10.404274
Coefficient of variation (CV)0.20693449
Kurtosis0.84026231
Mean50.278105
Median Absolute Deviation (MAD)7
Skewness0.50929463
Sum28554092
Variance108.24891
MonotonicityNot monotonic
2025-05-25T15:22:59.217887image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
47 25757
 
4.5%
48 25225
 
4.4%
44 25086
 
4.4%
42 23775
 
4.2%
50 23720
 
4.2%
51 22837
 
4.0%
52 21755
 
3.8%
53 20489
 
3.6%
46 20023
 
3.5%
45 19515
 
3.4%
Other values (76) 339741
59.8%
ValueCountFrequency (%)
0 70
< 0.1%
3 1
 
< 0.1%
4 1
 
< 0.1%
7 10
 
< 0.1%
8 43
 
< 0.1%
13 47
 
< 0.1%
14 56
 
< 0.1%
15 2
 
< 0.1%
16 21
 
< 0.1%
17 145
< 0.1%
ValueCountFrequency (%)
100 181
 
< 0.1%
96 149
 
< 0.1%
95 129
 
< 0.1%
93 113
 
< 0.1%
91 117
 
< 0.1%
90 352
0.1%
89 31
 
< 0.1%
88 7
 
< 0.1%
87 425
0.1%
86 497
0.1%

PUNT_LECTURA_CRITICA
Real number (ℝ)

High correlation 

Distinct80
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.92213
Minimum0
Maximum100
Zeros219
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size8.7 MiB
2025-05-25T15:22:59.270360image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile36
Q143
median49
Q356
95-th percentile67
Maximum100
Range100
Interquartile range (IQR)13

Descriptive statistics

Standard deviation9.535074
Coefficient of variation (CV)0.19099894
Kurtosis0.76435715
Mean49.92213
Median Absolute Deviation (MAD)6
Skewness0.34884923
Sum28351926
Variance90.917637
MonotonicityNot monotonic
2025-05-25T15:22:59.326029image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
46 34881
 
6.1%
47 33628
 
5.9%
50 32561
 
5.7%
43 31851
 
5.6%
53 29215
 
5.1%
54 27602
 
4.9%
49 26774
 
4.7%
51 24642
 
4.3%
44 24129
 
4.2%
40 23781
 
4.2%
Other values (70) 278859
49.1%
ValueCountFrequency (%)
0 219
< 0.1%
4 2
 
< 0.1%
7 8
 
< 0.1%
8 34
 
< 0.1%
9 4
 
< 0.1%
10 9
 
< 0.1%
11 4
 
< 0.1%
16 136
< 0.1%
17 1
 
< 0.1%
18 17
 
< 0.1%
ValueCountFrequency (%)
100 55
 
< 0.1%
99 5
 
< 0.1%
94 70
 
< 0.1%
93 208
 
< 0.1%
92 4
 
< 0.1%
91 18
 
< 0.1%
86 77
 
< 0.1%
85 719
0.1%
84 14
 
< 0.1%
81 230
 
< 0.1%

PUNT_GLOBAL
Real number (ℝ)

High correlation 

Distinct456
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean250.98529
Minimum0
Maximum492
Zeros4
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size8.7 MiB
2025-05-25T15:22:59.381979image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile182
Q1216
median246
Q3280
95-th percentile340
Maximum492
Range492
Interquartile range (IQR)64

Descriptive statistics

Standard deviation48.496796
Coefficient of variation (CV)0.19322565
Kurtosis0.61171879
Mean250.98529
Median Absolute Deviation (MAD)31
Skewness0.63938216
Sum1.4254032 × 108
Variance2351.9392
MonotonicityNot monotonic
2025-05-25T15:22:59.436948image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
230 5791
 
1.0%
232 5759
 
1.0%
242 5725
 
1.0%
243 5674
 
1.0%
245 5659
 
1.0%
235 5643
 
1.0%
237 5625
 
1.0%
228 5615
 
1.0%
248 5588
 
1.0%
238 5582
 
1.0%
Other values (446) 511262
90.0%
ValueCountFrequency (%)
0 4
< 0.1%
3 1
 
< 0.1%
5 1
 
< 0.1%
6 1
 
< 0.1%
8 2
< 0.1%
12 2
< 0.1%
13 1
 
< 0.1%
14 1
 
< 0.1%
15 2
< 0.1%
16 1
 
< 0.1%
ValueCountFrequency (%)
492 1
 
< 0.1%
490 1
 
< 0.1%
488 1
 
< 0.1%
483 2
< 0.1%
479 1
 
< 0.1%
478 2
< 0.1%
477 3
< 0.1%
476 4
< 0.1%
475 1
 
< 0.1%
473 2
< 0.1%

Interactions

2025-05-25T15:22:50.447819image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:21:59.583955image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:22:01.180935image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:22:05.693566image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:22:07.367338image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:22:09.011061image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:22:10.646311image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:22:12.312756image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:22:15.003071image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:22:17.012849image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:22:18.683065image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:22:20.312466image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:22:22.036434image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:22:25.105522image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:22:27.161137image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:22:28.942418image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:22:30.581512image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:22:32.198603image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:22:33.849412image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:22:35.536286image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:22:38.873636image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
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2025-05-25T15:22:50.151596image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:22:51.800680image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:22:00.949458image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:22:05.447358image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:22:07.120495image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:22:08.775073image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:22:10.407269image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:22:12.073190image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:22:14.657828image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:22:16.719728image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:22:18.435486image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:22:20.076761image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:22:21.808563image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:22:24.860785image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:22:26.867413image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:22:28.681988image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:22:30.328862image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:22:31.947897image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:22:33.610147image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:22:35.275464image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:22:38.602297image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:22:40.338432image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:22:42.033943image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:22:43.633872image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:22:45.227026image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:22:46.825553image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:22:48.583590image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:22:50.210366image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:22:51.862719image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:22:01.008380image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:22:05.504022image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:22:07.184938image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:22:08.836154image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:22:10.464672image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:22:12.133821image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:22:14.717628image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:22:16.805106image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:22:18.499494image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:22:20.135825image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:22:21.866767image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:22:24.923076image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:22:26.941808image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:22:28.749931image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:22:30.387935image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:22:32.006805image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:22:33.667450image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:22:35.337388image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:22:38.667726image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:22:40.401576image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:22:42.095431image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:22:43.691886image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:22:45.284280image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:22:46.883717image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:22:48.643312image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:22:50.271012image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:22:51.933835image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:22:01.065419image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:22:05.573085image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:22:07.244345image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:22:08.892013image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:22:10.521771image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:22:12.191719image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:22:14.780311image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:22:16.884002image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:22:18.556572image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:22:20.195577image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:22:21.922473image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:22:24.983627image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:22:27.015412image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:22:28.812397image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:22:30.458573image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:22:32.072188image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:22:33.729925image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:22:35.412718image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:22:38.735742image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:22:40.461168image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:22:42.158203image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:22:43.752920image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:22:45.343750image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:22:46.941850image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:22:48.703309image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:22:50.328919image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:22:51.991765image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:22:01.122517image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:22:05.629762image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:22:07.305812image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:22:08.952640image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:22:10.590114image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:22:12.250251image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:22:14.853915image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:22:16.947849image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:22:18.620415image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:22:20.253898image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:22:21.976358image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:22:25.046254image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:22:27.097917image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:22:28.880643image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:22:30.517361image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:22:32.136228image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:22:33.787919image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:22:35.475036image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:22:38.801555image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:22:40.523736image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:22:42.225730image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:22:43.809518image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:22:45.399648image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:22:47.000217image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:22:48.762090image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:22:50.385253image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Correlations

2025-05-25T15:22:59.507999image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
COLE_AREA_UBICACIONCOLE_BILINGUECOLE_CALENDARIOCOLE_CARACTERCOLE_COD_DANE_ESTABLECIMIENTOCOLE_COD_DANE_SEDECOLE_COD_DEPTO_UBICACIONCOLE_COD_MCPIO_UBICACIONCOLE_DEPTO_UBICACIONCOLE_GENEROCOLE_JORNADACOLE_MCPIO_UBICACIONCOLE_NATURALEZACOLE_NOMBRE_ESTABLECIMIENTOCOLE_NOMBRE_SEDECOLE_SEDE_PRINCIPALDESEMP_INGLESESTU_COD_RESIDE_DEPTOESTU_COD_RESIDE_MCPIOESTU_DEPTO_RESIDEESTU_FECHANACIMIENTOESTU_GENEROESTU_MCPIO_RESIDEESTU_PAIS_RESIDEESTU_TIPODOCUMENTOFAMI_CUARTOSHOGARFAMI_EDUCACIONMADREFAMI_EDUCACIONPADREFAMI_ESTRATOVIVIENDAFAMI_PERSONASHOGARFAMI_TIENEAUTOMOVILFAMI_TIENECOMPUTADORFAMI_TIENEINTERNETFAMI_TIENELAVADORAPERIODOPUNT_C_NATURALESPUNT_GLOBALPUNT_INGLESPUNT_LECTURA_CRITICAPUNT_MATEMATICASPUNT_SOCIALES_CIUDADANAS
COLE_AREA_UBICACION1.0000.1370.0280.1200.8200.6370.1960.1900.1930.0660.1250.1980.1630.2080.1660.0550.1110.0000.1800.1840.0040.0200.1860.0010.0370.0530.2240.2310.1520.0570.0880.2430.2740.2240.0300.1120.1350.1140.1320.1220.116
COLE_BILINGUE0.1371.0000.2150.1350.1370.1200.0480.0480.0690.0470.0980.0450.1230.0810.0870.0040.1550.0030.0470.0630.0000.0090.0420.0200.0280.0200.1110.0710.2100.0130.0700.0320.0300.0230.2280.1200.1300.1610.0960.1020.111
COLE_CALENDARIO0.0280.2151.0000.0860.2520.2420.1700.1710.1690.0530.1300.1510.2910.1700.1420.0270.1960.0080.1690.1680.0000.0130.1410.0250.0660.0440.1840.1200.3050.0320.1430.0760.0900.0630.7000.1920.1820.2060.1490.1370.168
COLE_CARACTER0.1200.1350.0861.0000.2160.1900.1410.1400.1590.0350.1210.1320.3840.1790.1300.0560.0730.0000.1400.1480.0030.0260.1330.0040.0350.0140.0710.0600.0920.0180.0880.0650.0900.0760.1280.0520.0580.0740.0450.0480.047
COLE_COD_DANE_ESTABLECIMIENTO0.8200.1370.2520.2161.0000.9940.3620.3580.3520.095-0.2470.0460.920-0.134-0.1050.1080.1600.3560.3520.345-0.0010.0320.0370.009-0.0530.007-0.117-0.0990.1450.0140.2080.2200.2570.2000.3300.0630.0650.1080.0570.0570.062
COLE_COD_DANE_SEDE0.6370.1200.2420.1900.9941.0000.3610.3570.3510.080-0.2470.0480.854-0.137-0.1040.0650.1400.3550.3510.344-0.0010.0310.0380.009-0.0540.006-0.116-0.0980.1440.0130.1850.1780.2120.1490.3130.0620.0650.1070.0560.0560.061
COLE_COD_DEPTO_UBICACION0.1960.0480.1700.1410.3620.3611.0000.9980.9470.0780.0270.2230.2060.1620.1020.0780.0660.9930.9910.940-0.0000.0150.226-0.000-0.013-0.014-0.034-0.020-0.1820.0020.0710.1620.2070.1720.160-0.018-0.039-0.056-0.046-0.039-0.032
COLE_COD_MCPIO_UBICACION0.1900.0480.1710.1400.3580.3570.9981.0000.9450.0750.0220.2410.2030.1680.1080.0730.0660.9910.9930.938-0.0000.0160.243-0.001-0.013-0.014-0.035-0.020-0.1910.0020.0680.1610.2060.1720.160-0.022-0.044-0.061-0.051-0.043-0.036
COLE_DEPTO_UBICACION0.1930.0690.1690.1590.3520.3510.9470.9451.0000.0820.0180.2020.1980.1550.0920.0730.0780.9390.9380.992-0.0010.0180.2060.000-0.004-0.015-0.027-0.014-0.155-0.0000.0890.1530.1820.1610.162-0.011-0.031-0.048-0.037-0.033-0.027
COLE_GENERO0.0660.0470.0530.0350.0950.0800.0780.0750.0821.0000.1060.0710.1160.0980.1060.0290.1390.0000.0750.0800.0010.1180.0670.0050.0470.0300.1380.1080.1410.0230.0960.0760.0870.0580.0330.1180.1310.1390.1120.1180.112
COLE_JORNADA0.1250.0980.1300.121-0.247-0.2470.0270.0220.0180.1061.0000.0330.4450.0960.0410.0570.1790.0320.0280.024-0.0010.0150.046-0.008-0.210-0.0040.0820.083-0.2040.0060.1820.1320.1280.0880.149-0.258-0.275-0.243-0.230-0.252-0.237
COLE_MCPIO_UBICACION0.1980.0450.1510.1320.0460.0480.2230.2410.2020.0710.0331.0000.1930.1870.2340.0610.0740.2180.2350.1970.0010.0140.939-0.004-0.028-0.004-0.026-0.016-0.2070.0020.0910.1520.1820.1410.138-0.096-0.107-0.106-0.101-0.094-0.086
COLE_NATURALEZA0.1630.1230.2910.3840.9200.8540.2060.2030.1980.1160.4450.1931.0000.6370.4070.0910.3130.0000.2070.2110.0000.0450.2020.0180.0490.1130.3550.3020.3950.1000.3080.2470.2990.2060.3140.2270.2480.3140.2060.2100.202
COLE_NOMBRE_ESTABLECIMIENTO0.2080.0810.1700.179-0.134-0.1370.1620.1680.1550.0980.0960.1870.6371.0000.6030.0720.1280.1620.1680.1550.0000.0250.181-0.004-0.023-0.0000.0340.042-0.277-0.0030.1750.1910.2280.1620.215-0.153-0.167-0.160-0.148-0.149-0.143
COLE_NOMBRE_SEDE0.1660.0870.1420.130-0.105-0.1040.1020.1080.0920.1060.0410.2340.4070.6031.0000.1230.1130.1020.1070.092-0.0010.0200.228-0.004-0.0110.0030.0220.029-0.187-0.0020.1490.1410.1650.1130.149-0.087-0.094-0.097-0.084-0.082-0.080
COLE_SEDE_PRINCIPAL0.0550.0040.0270.0560.1080.0650.0780.0730.0730.0290.0570.0610.0910.0720.1231.0000.0350.0000.0740.0760.0030.0080.0580.0050.0210.0130.0530.0500.0440.0130.0300.0370.0440.0370.0280.0340.0390.0350.0350.0360.034
DESEMP_INGLES0.1110.1550.1960.0730.1600.1400.0660.0660.0780.1390.1790.0740.3130.1280.1130.0351.0000.0050.0700.0840.0030.0280.0830.0210.0980.0500.2480.1910.2580.0450.2340.1950.2150.1470.1920.3710.4460.8150.3490.3550.345
ESTU_COD_RESIDE_DEPTO0.0000.0030.0080.0000.3560.3550.9930.9910.9390.0000.0320.2180.0000.1620.1020.0000.0051.0000.9980.947-0.0000.0000.234-0.001-0.014-0.015-0.031-0.017-0.1870.0030.0020.0000.0000.0000.003-0.022-0.044-0.061-0.050-0.043-0.036
ESTU_COD_RESIDE_MCPIO0.1800.0470.1690.1400.3520.3510.9910.9930.9380.0750.0280.2350.2070.1680.1070.0740.0700.9981.0000.945-0.0000.0160.252-0.001-0.014-0.014-0.032-0.017-0.1960.0030.0690.1620.2070.1720.161-0.026-0.049-0.066-0.055-0.047-0.040
ESTU_DEPTO_RESIDE0.1840.0630.1680.1480.3450.3440.9400.9380.9920.0800.0240.1970.2110.1550.0920.0760.0840.9470.9451.000-0.0010.0180.213-0.001-0.005-0.015-0.025-0.012-0.160-0.0000.1020.1760.1960.1570.163-0.015-0.036-0.053-0.041-0.037-0.031
ESTU_FECHANACIMIENTO0.0040.0000.0000.003-0.001-0.001-0.000-0.000-0.0010.001-0.0010.0010.0000.000-0.0010.0030.003-0.000-0.000-0.0011.0000.0030.001-0.0010.0090.0010.0030.0030.0030.0010.0020.0010.0020.0020.0000.0010.0020.0020.0020.0020.001
ESTU_GENERO0.0200.0090.0130.0260.0320.0310.0150.0160.0180.1180.0150.0140.0450.0250.0200.0080.0280.0000.0160.0180.0031.0000.0140.0000.0860.0310.0370.0340.0260.0130.0260.0200.0250.0230.0290.0820.0660.0290.0200.1000.059
ESTU_MCPIO_RESIDE0.1860.0420.1410.1330.0370.0380.2260.2430.2060.0670.0460.9390.2020.1810.2280.0580.0830.2340.2520.2130.0010.0141.000-0.005-0.028-0.003-0.017-0.009-0.2180.0030.1010.1580.1880.1460.134-0.101-0.112-0.114-0.105-0.098-0.091
ESTU_PAIS_RESIDE0.0010.0200.0250.0040.0090.009-0.000-0.0010.0000.005-0.008-0.0040.018-0.004-0.0040.0050.021-0.001-0.001-0.001-0.0010.000-0.0051.000-0.0100.001-0.005-0.0050.0160.0010.0130.0070.0090.0060.0270.0110.0120.0150.0110.0110.008
ESTU_TIPODOCUMENTO0.0370.0280.0660.035-0.053-0.054-0.013-0.013-0.0040.047-0.210-0.0280.049-0.023-0.0110.0210.098-0.014-0.014-0.0050.0090.086-0.028-0.0101.000-0.0050.0710.0590.111-0.0380.0740.1140.1020.0820.0980.2720.2900.2050.2510.2690.236
FAMI_CUARTOSHOGAR0.0530.0200.0440.0140.0070.006-0.014-0.014-0.0150.030-0.004-0.0040.113-0.0000.0030.0130.050-0.015-0.014-0.0150.0010.031-0.0030.001-0.0051.0000.0080.0080.014-0.0120.1950.2080.2080.1950.0510.0100.0130.0130.0120.0120.012
FAMI_EDUCACIONMADRE0.2240.1110.1840.071-0.117-0.116-0.034-0.035-0.0270.1380.082-0.0260.3550.0340.0220.0530.248-0.031-0.032-0.0250.0030.037-0.017-0.0050.0710.0081.0000.385-0.022-0.0200.7590.7590.7640.7450.189-0.017-0.014-0.042-0.009-0.012-0.020
FAMI_EDUCACIONPADRE0.2310.0710.1200.060-0.099-0.098-0.020-0.020-0.0140.1080.083-0.0160.3020.0420.0290.0500.191-0.017-0.017-0.0120.0030.034-0.009-0.0050.0590.0080.3851.000-0.037-0.0280.3130.3300.3400.3040.121-0.031-0.029-0.050-0.024-0.025-0.034
FAMI_ESTRATOVIVIENDA0.1520.2100.3050.0920.1450.144-0.182-0.191-0.1550.141-0.204-0.2070.395-0.277-0.1870.0440.258-0.187-0.196-0.1600.0030.026-0.2180.0160.1110.014-0.022-0.0371.0000.0050.3890.3420.3700.3200.3250.3500.3940.3740.3590.3440.344
FAMI_PERSONASHOGAR0.0570.0130.0320.0180.0140.0130.0020.002-0.0000.0230.0060.0020.100-0.003-0.0020.0130.0450.0030.003-0.0000.0010.0130.0030.001-0.038-0.012-0.020-0.0280.0051.0000.3420.3450.3440.3420.049-0.014-0.012-0.002-0.007-0.017-0.006
FAMI_TIENEAUTOMOVIL0.0880.0700.1430.0880.2080.1850.0710.0680.0890.0960.1820.0910.3080.1750.1490.0300.2340.0020.0690.1020.0020.0260.1010.0130.0740.1950.7590.3130.3890.3421.0000.7420.7460.7350.1590.1920.2090.2360.1760.1890.176
FAMI_TIENECOMPUTADOR0.2430.0320.0760.0650.2200.1780.1620.1610.1530.0760.1320.1520.2470.1910.1410.0370.1950.0000.1620.1760.0010.0200.1580.0070.1140.2080.7590.3300.3420.3450.7421.0000.8580.7760.0940.1950.2160.1980.1970.1930.189
FAMI_TIENEINTERNET0.2740.0300.0900.0900.2570.2120.2070.2060.1820.0870.1280.1820.2990.2280.1650.0440.2150.0000.2070.1960.0020.0250.1880.0090.1020.2080.7640.3400.3700.3440.7460.8581.0000.7760.1140.1970.2200.2170.2030.1960.191
FAMI_TIENELAVADORA0.2240.0230.0630.0760.2000.1490.1720.1720.1610.0580.0880.1410.2060.1620.1130.0370.1470.0000.1720.1570.0020.0230.1460.0060.0820.1950.7450.3040.3200.3420.7350.7760.7761.0000.0770.1330.1510.1490.1400.1360.128
PERIODO0.0300.2280.7000.1280.3300.3130.1600.1600.1620.0330.1490.1380.3140.2150.1490.0280.1920.0030.1610.1630.0000.0290.1340.0270.0980.0510.1890.1210.3250.0490.1590.0940.1140.0771.0000.2150.1730.2040.1380.1170.168
PUNT_C_NATURALES0.1120.1200.1920.0520.0630.062-0.018-0.022-0.0110.118-0.258-0.0960.227-0.153-0.0870.0340.371-0.022-0.026-0.0150.0010.082-0.1010.0110.2720.010-0.017-0.0310.350-0.0140.1920.1950.1970.1330.2151.0000.9020.5620.7280.7630.767
PUNT_GLOBAL0.1350.1300.1820.0580.0650.065-0.039-0.044-0.0310.131-0.275-0.1070.248-0.167-0.0940.0390.446-0.044-0.049-0.0360.0020.066-0.1120.0120.2900.013-0.014-0.0290.394-0.0120.2090.2160.2200.1510.1730.9021.0000.6460.8770.8880.908
PUNT_INGLES0.1140.1610.2060.0740.1080.107-0.056-0.061-0.0480.139-0.243-0.1060.314-0.160-0.0970.0350.815-0.061-0.066-0.0530.0020.029-0.1140.0150.2050.013-0.042-0.0500.374-0.0020.2360.1980.2170.1490.2040.5620.6461.0000.5470.5370.548
PUNT_LECTURA_CRITICA0.1320.0960.1490.0450.0570.056-0.046-0.051-0.0370.112-0.230-0.1010.206-0.148-0.0840.0350.349-0.050-0.055-0.0410.0020.020-0.1050.0110.2510.012-0.009-0.0240.359-0.0070.1760.1970.2030.1400.1380.7280.8770.5471.0000.7010.767
PUNT_MATEMATICAS0.1220.1020.1370.0480.0570.056-0.039-0.043-0.0330.118-0.252-0.0940.210-0.149-0.0820.0360.355-0.043-0.047-0.0370.0020.100-0.0980.0110.2690.012-0.012-0.0250.344-0.0170.1890.1930.1960.1360.1170.7630.8880.5370.7011.0000.721
PUNT_SOCIALES_CIUDADANAS0.1160.1110.1680.0470.0620.061-0.032-0.036-0.0270.112-0.237-0.0860.202-0.143-0.0800.0340.345-0.036-0.040-0.0310.0010.059-0.0910.0080.2360.012-0.020-0.0340.344-0.0060.1760.1890.1910.1280.1680.7670.9080.5480.7670.7211.000

Missing values

2025-05-25T15:22:52.183506image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
A simple visualization of nullity by column.
2025-05-25T15:22:53.154592image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

PERIODOESTU_TIPODOCUMENTOCOLE_AREA_UBICACIONCOLE_BILINGUECOLE_CALENDARIOCOLE_CARACTERCOLE_COD_DANE_ESTABLECIMIENTOCOLE_COD_DANE_SEDECOLE_COD_DEPTO_UBICACIONCOLE_COD_MCPIO_UBICACIONCOLE_DEPTO_UBICACIONCOLE_GENEROCOLE_JORNADACOLE_MCPIO_UBICACIONCOLE_NATURALEZACOLE_NOMBRE_ESTABLECIMIENTOCOLE_NOMBRE_SEDECOLE_SEDE_PRINCIPALESTU_COD_RESIDE_DEPTOESTU_COD_RESIDE_MCPIOESTU_DEPTO_RESIDEESTU_FECHANACIMIENTOESTU_GENEROESTU_MCPIO_RESIDEESTU_PAIS_RESIDEFAMI_CUARTOSHOGARFAMI_EDUCACIONMADREFAMI_EDUCACIONPADREFAMI_ESTRATOVIVIENDAFAMI_PERSONASHOGARFAMI_TIENEAUTOMOVILFAMI_TIENECOMPUTADORFAMI_TIENEINTERNETFAMI_TIENELAVADORADESEMP_INGLESPUNT_INGLESPUNT_MATEMATICASPUNT_SOCIALES_CIUDADANASPUNT_C_NATURALESPUNT_LECTURA_CRITICAPUNT_GLOBAL
020152810001.053760e+111053760001135537612442213134459595.05376.03102793364281921173208301149.052.0494552247
120152810031.110010e+111110010194111111001421100117986057911.011001.0611783369919414103198511044.072.0606259309
220152802002.413570e+1124135700069341413571720400165878401941.041357.020557933610311921292138500044.046.0384238206
320152812003.110011e+113110010897101111001420100024612333911.011001.06677233699192137208511044.047.0574758258
420152800002.134300e+112134300026121313430521485148249240913.013430.0777573354901921292208301155.044.0455341232
520152811001.860010e+1118600100017586860012324525136655317986.086001.0263225335529194182218501044.046.0535554257
620152810031.050010e+111050010001085500110151215485621995.05088.03128463358519101314318511152.047.0535851261
720152810001.056600e+111056600002715566012178317346888995.05660.03142733578519101283218511043.047.0442938199
820152800032.118500e+11211850001121111100142110012323566911.011001.06558233599194117218311157.046.0505241240
920152810031.660010e+1116600100609166660012524620124703613966.066001.02865523356211921854013511264.049.0596662297
PERIODOESTU_TIPODOCUMENTOCOLE_AREA_UBICACIONCOLE_BILINGUECOLE_CALENDARIOCOLE_CARACTERCOLE_COD_DANE_ESTABLECIMIENTOCOLE_COD_DANE_SEDECOLE_COD_DEPTO_UBICACIONCOLE_COD_MCPIO_UBICACIONCOLE_DEPTO_UBICACIONCOLE_GENEROCOLE_JORNADACOLE_MCPIO_UBICACIONCOLE_NATURALEZACOLE_NOMBRE_ESTABLECIMIENTOCOLE_NOMBRE_SEDECOLE_SEDE_PRINCIPALESTU_COD_RESIDE_DEPTOESTU_COD_RESIDE_MCPIOESTU_DEPTO_RESIDEESTU_FECHANACIMIENTOESTU_GENEROESTU_MCPIO_RESIDEESTU_PAIS_RESIDEFAMI_CUARTOSHOGARFAMI_EDUCACIONMADREFAMI_EDUCACIONPADREFAMI_ESTRATOVIVIENDAFAMI_PERSONASHOGARFAMI_TIENEAUTOMOVILFAMI_TIENECOMPUTADORFAMI_TIENEINTERNETFAMI_TIENELAVADORADESEMP_INGLESPUNT_INGLESPUNT_MATEMATICASPUNT_SOCIALES_CIUDADANASPUNT_C_NATURALESPUNT_LECTURA_CRITICAPUNT_GLOBAL
57045420152800004.156320e+114156320005161515632621726141452199915.015104.0865513361041921110208300154.055.0574656268
57045520152810021.682760e+1116827600072068682762721314110742366968.068276.03010677335319191011102218511038.037.0543850221
57045620152810003.410011e+115410010080974141001172155603691050941.041001.0202683355601910139318511152.047.0364344216
57045720152802002.192560e+1121925600019119192561021295134194892919.019256.01275443362981910128208300155.058.0626252291
57045820152010031.768920e+11176892000281767689230241016140588721976.076892.033729833510191910139218301041.049.0514846240
57045920152810001.230010e+1112300100374123230011321537166427778923.023001.0151964335542194139208511150.050.0594554259
57046020152810001.050010e+111050010013685500112151214562618895.05001.03101323355151911115348511150.039.0565163260
57046120152810031.200130e+111200130004712020013112113163046754920.020013.0131067733512194127218301149.039.0344943209
57046220152810031.448740e+1114487400051744448741821996176946850944.044874.0217955335998191114102198300154.047.0564858262
57046320152800022.152720e+112152720001971515272620308137732346915.015272.08237833531419111183218300155.052.0635965297